Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations

Matti Juhani Koivisto*, Konstantinos Plakas, Ernesto Rodrigo Hurtado Ellmann, Neil Davis, Poul Sørensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


With increasing wind installations, there is a need to analyse wind generation variability in detail. This paper applies the reanalysis approach for modelling the variability; however, with two important additions. Firstly, high-resolution microscale data is combined with mesoscale reanalysis time series to model local variability in wind. Secondly, as there are often missing technical parameters in large-scale wind power plant datasets, machine learning is used to estimate the missing values. It is shown that such detailed modelling leads to more accurate simulations than a baseline model when compared to historical data from multiple European countries. In addition, applicability of the methodology for analysing future scenarios with changing wind installations is demonstrated.
Original languageEnglish
Article number106638
JournalElectric Power Systems Research
Number of pages7
Publication statusPublished - 2021


  • Microscale
  • Random forest
  • Reanalysis
  • Variability
  • Wind

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